The Role of Data Science in Agriculture
December 17, 2024
My name is Orville D. Hombrebueno. I am a Math teacher at the College of Teacher Education, Nueva Vizcaya State University.
Data Science
Data Science in Agriculture
Forecasting Vegetable Prices
SARAI
Challenges
Conclusion
Data science is the process of extracting insights from data.
Collect data from various sources (weather, soil, satellite images).
Clean and prepare the data.
Analyze the data to identify patterns and trends.
Use the insights to make informed decisions.
Predictive Analytics: Forecast crop yields, potential problems, and optimal harvest times.
Precision Agriculture: Optimize resource usage (water, fertilizer, pesticides) for maximum efficiency.
Disease and Pest Detection: Identify and address threats early on.
Market Analysis: Analyze market trends to make informed farming decisions.
Forecasting Monthly Vegetable Prices in the Province of Nueva Vizcaya
by J. N. P. Alap, G. G. Gonzales, E. J. Jimenez, C. D. Pastores,
12 Vegetables:
broccoli, cabbage, carrot, cauliflower, celery, chayote (bunga), cucumber, gabi (galyang), pepper (sultan), pepper (taiwan), potato, wombok
Figure 1: Monthly Prices for Pepper (Taiwan)
Figure 2: STL Decomposition of Pepper (Taiwan)
Figure 3: Forecast for Pepper (Taiwan)
Here are three challenges identified by Ibrahim (2023) when using data science in agriculture.
Lack of understanding.
Availability of data.
Lack of skills.
Data science can improve farming.
There are challenges.
The future is bright.
Training-Workshop on Convergence of Gender, Livelihood, and Data-Driven Agriculture